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• W2893766950 abstract "In alumina rotary kiln production, adjusting the coal feeding rate is the main way to maintain sintering temperature stability during the sintering process, which plays a critical role in improving production quality and reducing energy consumption. In this paper, a novel integrated method (termed PSR-PCA-HMM) is proposed to predict the coal feeding state for optimal control by integrating principal component analysis (PCA) and the hidden Markov model (HMM) based on phase space reconstruction (PSR). First, the thermal signals in rotary kilns are shown to have obvious chaotic characteristics. Second, PSR is utilized to extract the features of the sintering process in a rotary kiln, and PCA is proposed to efficiently reduce the redundancy of the high-dimensional feature space reconstructed by the PSR. Then, considering the nonlinear dynamic characteristic of the sintering process, three HMM models are built to capture the nonlinear dynamic relationship between thermal variables and the corresponding coal feeding state. Finally, the posterior probabilities with respect to the three HMM models are estimated by using the forward algorithm, and the final prediction of coal feeding is determined by the maximized likelihood estimation. Based on field data, the application results indicate that the PSR-PCA-HMM method can significantly improve prediction performance and help realize stable closed-loop control for the sintering temperature." @default.
• W2893766950 created "2018-10-05" @default.
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• W2893766950 date "2018-12-01" @default.
• W2893766950 modified "2023-10-11" @default.
• W2893766950 title "Prediction of coal feeding during sintering in a rotary kiln based on statistical learning in the phase space" @default.
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• W2893766950 doi "https://doi.org/10.1016/j.isatra.2018.09.015" @default.
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• W2893766950 hasPublicationYear "2018" @default.
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